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dc.contributor.advisorTempone, Raul
dc.contributor.authorHappola, Juho
dc.date.accessioned2017-10-23T05:22:45Z
dc.date.available2018-10-08T00:00:00Z
dc.date.issued2017-09-19
dc.identifier.citationHappola, J. (2017). Efficient Numerical Methods for Stochastic Differential Equations in Computational Finance. KAUST Research Repository. https://doi.org/10.25781/KAUST-71T82
dc.identifier.doi10.25781/KAUST-71T82
dc.identifier.urihttp://hdl.handle.net/10754/625924
dc.description.abstractStochastic Differential Equations (SDE) offer a rich framework to model the probabilistic evolution of the state of a system. Numerical approximation methods are typically needed in evaluating relevant Quantities of Interest arising from such models. In this dissertation, we present novel effective methods for evaluating Quantities of Interest relevant to computational finance when the state of the system is described by an SDE.
dc.language.isoen
dc.subjectoptions
dc.subjectStochastic Differential Equations
dc.subjectNumerical Methods
dc.titleEfficient Numerical Methods for Stochastic Differential Equations in Computational Finance
dc.typeDissertation
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.rights.embargodate2018-10-08
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberAlouini, Mohamed-Slim
dc.contributor.committeememberGomes, Diogo A.
dc.contributor.committeememberDjehiche, Boualem
dc.contributor.committeememberMordecki, Ernesto
dc.contributor.committeememberZubelli, Jorge
thesis.degree.disciplineApplied Mathematics and Computational Science
thesis.degree.nameDoctor of Philosophy
dc.rights.accessrightsAt the time of archiving, the student author of this dissertation opted to temporarily restrict access to it. The full text of this dissertation became available to the public after the expiration of the embargo on 2018-10-08.
refterms.dateFOA2018-10-08T00:00:00Z


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